A dynamic constrained multi-objective evolutionary algorithm with a dual prediction mechanism
•Biased sampling using predicted similarity across multiple environments.•Improved prediction method applied as an initialization and search strategy.•Strategy to reuse evaluation budget from environmental detection.•Dual-population static optimizer for dynamic constrained multi-objective problems....
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| Published in: | Expert systems with applications Vol. 297; p. 129304 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.02.2026
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| ISSN: | 0957-4174 |
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| Abstract | •Biased sampling using predicted similarity across multiple environments.•Improved prediction method applied as an initialization and search strategy.•Strategy to reuse evaluation budget from environmental detection.•Dual-population static optimizer for dynamic constrained multi-objective problems.
The design of evolutionary algorithms for dynamic constrained multi-objective optimization problems is a significant research area, driven by the need for real-time adaptive solutions in complex real-world systems. To address this, this paper proposes DP-DCMOA, an algorithm that introduces a novel hybrid dynamic response by synergistically combining memory-based sampling and prediction-based generation. The algorithm’s novelty lies in its dual prediction approach. For the memory-based component, it leverages the predictable time-series properties of environmental similarity to guide a robust biased sampling from historical archives. For the prediction-based component, it employs an enhanced trajectory predictor to generate new, high-quality solutions. Furthermore, a key innovation is the integration of this trajectory predictor as a continuous search operator within the static optimizer, moving beyond its traditional role as a one-shot re-initialization tool. This dual-response system is supported by a specialized dual-population static optimizer. The effectiveness of the framework is validated through extensive experiments, demonstrating significant performance advantages over state-of-the-art algorithms and a high ranking in academic competitions. |
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| AbstractList | •Biased sampling using predicted similarity across multiple environments.•Improved prediction method applied as an initialization and search strategy.•Strategy to reuse evaluation budget from environmental detection.•Dual-population static optimizer for dynamic constrained multi-objective problems.
The design of evolutionary algorithms for dynamic constrained multi-objective optimization problems is a significant research area, driven by the need for real-time adaptive solutions in complex real-world systems. To address this, this paper proposes DP-DCMOA, an algorithm that introduces a novel hybrid dynamic response by synergistically combining memory-based sampling and prediction-based generation. The algorithm’s novelty lies in its dual prediction approach. For the memory-based component, it leverages the predictable time-series properties of environmental similarity to guide a robust biased sampling from historical archives. For the prediction-based component, it employs an enhanced trajectory predictor to generate new, high-quality solutions. Furthermore, a key innovation is the integration of this trajectory predictor as a continuous search operator within the static optimizer, moving beyond its traditional role as a one-shot re-initialization tool. This dual-response system is supported by a specialized dual-population static optimizer. The effectiveness of the framework is validated through extensive experiments, demonstrating significant performance advantages over state-of-the-art algorithms and a high ranking in academic competitions. |
| ArticleNumber | 129304 |
| Author | Zhang, Lili Deng, Libao Li, Chunlei Yang, Zhe Qin, Yifan |
| Author_xml | – sequence: 1 givenname: Zhe orcidid: 0000-0002-2408-5956 surname: Yang fullname: Yang, Zhe email: 22b305001@stu.hit.edu.cn organization: School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, China – sequence: 2 givenname: Libao orcidid: 0000-0003-0076-4054 surname: Deng fullname: Deng, Libao email: dlb@hit.edu.cn organization: School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, China – sequence: 3 givenname: Chunlei orcidid: 0000-0002-5102-0330 surname: Li fullname: Li, Chunlei email: 21b905036@stu.hit.edu.cn organization: School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, China – sequence: 4 givenname: Yifan surname: Qin fullname: Qin, Yifan email: 23B905004@stu.hit.edu.cn organization: School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, China – sequence: 5 givenname: Lili surname: Zhang fullname: Zhang, Lili email: lili.zhang@dcu.ie organization: School of Computing, Dublin City University, Dublin, D09 V209, Ireland |
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